Spring 2027

Fundamentals of Industrial Data Analytics

Fundamentals of Industrial Data Analytics covers using data analytics for data-driven decisions, emphasizing application to industrial engineering problems via modeling strategies.

offering recorded3 credits
Recorded instructors · Spring 2027 Tina Xu3.2/5Kaibo Liu

Summary

1 / 6

Past student reviews · Tina Xu

Weekly homework consisted of about four to five questions covering the lecture material. Professor Xu also offered a 3% extra credit opportunity on the overall grade.

What you’ll be able to do

  • Apply fundamental knowledge of industrial data analytics concepts, problems, and techniques

  • Integrate data analytics techniques with industrial and systems engineering domain knowledge to appropriately formulate problem statements and facilitate decision making

  • Implement software programming skills to perform data analysis

  • Apply industrial data analytics methods and tools to solve real-world industrial engineering problems

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(I SY E 210,B M E 325,E C E 331,STAT 311, 324,MATH/​STAT 309, 409, 431,MATH 331, or 531), graduate/professional standing, or member of Engineering Guest Students

ISYE 412

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.38/5 from 13 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.5/5 raw quality · 2.5/5 difficulty · 6 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Tina Xu

Tina Xu offered a 3% extra credit and weekly homework in ISYE 412, with students describing the course as an easy A if you attended lectures and studied. She provided example problems in every lecture and gave clear grading, though some found the lectures dull.

Recent recorded grades — Spring 2024: 3.91 GPA, 95.7% A/AB (n=23 letter grades); Spring 2025: 3.60 GPA, 75.0% A/AB (n=52 letter grades); Spring 2026: 3.38 GPA, 60.0% A/AB (n=40 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

KAIBO LIU is recorded teaching in Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 100
LAB 301Classroom Instruction0 / 50
LAB 302Classroom Instruction0 / 50
LEC 003Online Only0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Tina Xu

Students describe the course as manageable if you attend lectures and complete homework. Grading is clear, and exams align closely with the material outlined by Professor Xu.

Recent recorded grades — Spring 2025: 3.71 GPA, 82.1% A/AB (n=78 letter grades); Fall 2025: 3.50 GPA, 71.4% A/AB (n=42 letter grades); Spring 2026: 3.58 GPA, 72.9% A/AB (n=59 letter grades).

difficulty & workload

Past student reviews · Tina Xu

Weekly homework consisted of about four to five questions covering the lecture material. Professor Xu also offered a 3% extra credit opportunity on the overall grade.

Past student reviews · Tina Xu

Some students found lectures boring because Professor Xu read from slides. Her handwritten examples were often difficult to read, which made understanding the in-class problems challenging.

Topics

  • Industrial engineering applications
  • Modeling strategies and analytics principles
  • Problem formulation and decision making

Skills

  • Industrial data analytics concepts and techniques
  • Software programming for data analysis
  • Solving real-world industrial engineering problems

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

6 earlier forecasts · 0.14 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
ISYE 412 · Spring 2027

Fundamentals of Industrial Data Analytics

Recorded 2026-10-29
Raw records 
[
  {
    "run_id": "20261029T213040-0fa85c20",
    "semester": "1274",
    "observed_at": "2026-10-29 21:30:40.248904+00:00",
    "offering_id": "1274:490:024587",
    "course_id": "ISYE 412",
    "course_uid": "course_4e8831b837f359daa4b2cadc",
    "term_id": "1274",
    "source_course_id": "024587",
    "source_subject_id": "490",
    "title": "Fundamentals of Industrial Data Analytics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-29

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "98394248846a093e6e58064d9c75924d75d42d7e5a4321e4624576f3420f576d",
  "requirements_status": "valid",
  "dataset_revision": "c9cd06eaa45f9ddf7e69faa38b89ff3d3dd995dd",
  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}